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BigQuery schema generator

Project description

This script generates the BigQuery schema from the newline-delimited JSON data records on the STDIN. The BigQuery data importer (bq load) uses only the first 100 lines when the schema auto-detection feature is enabled. In contrast, this script uses all data records to generate the schema.


$ generate-schema < > file.schema.json

Version: 0.3 (2018-12-17)


Data can be imported into BigQuery using the bq command line tool. It accepts a number of data formats including CSV or newline-delimited JSON. The data can be loaded into an existing table or a new table can be created during the loading process. The structure of the table is defined by its schema. The table’s schema can be defined manually or the schema can be auto-detected.

When the auto-detect feature is used, the BigQuery data importer examines only the first 100 records of the input data. In many cases, this is sufficient because the data records were dumped from another database and the exact schema of the source table was known. However, for data extracted from a service (e.g. using a REST API) the record fields could have been organically added at later dates. In this case, the first 100 records do not contain fields which are present in later records. The bq load auto-detection fails and the data fails to load.

The bq load tool does not support the ability to process the entire dataset to determine a more accurate schema. This script fills in that gap. It processes the entire dataset given in the STDIN and outputs the BigQuery schema in JSON format on the STDOUT. This schema file can be fed back into the bq load tool to create a table that is more compatible with the data fields in the input dataset.


Install from PyPI repository using pip3. If you want to install the package for your entire system globally, use

$ sudo -H pip3 install bigquery_schema_generator

If you are using a virtual environment (such as venv), then you don’t need the sudo coommand, and you can just type:

$ pip3 install bigquery_schema_generator

A successful install should print out the following:

Collecting bigquery-schema-generator
Installing collected packages: bigquery-schema-generator
Successfully installed bigquery-schema-generator-0.1.4

The shell script generate-schema is installed in the same directory as pip3.

Ubuntu Linux

Under Ubuntu Linux, you should find the generate-schema script at /usr/local/bin/generate-schema.


If you installed Python from Python Releases for Mac OS X, then /usr/local/bin/pip3 is a symlink to /Library/Frameworks/Python.framework/Versions/3.6/bin/pip3. So generate-schema is installed at /Library/Frameworks/Python.framework/Versions/3.6/bin/generate-schema.

The Python installer updates $HOME/.bash_profile to add /Library/Frameworks/Python.framework/Versions/3.6/bin to the $PATH environment variable. So you should be able to run the generate-schema command without typing in the full path.


The script accepts a newline-delimited JSON data file on the STDIN. (CSV is not supported currently.) It scans every record in the input data file to deduce the table’s schema. It prints the JSON formatted schema file on the STDOUT. There are at least 3 ways to run this script:

1) Shell script

If you installed using pip3, then it should have installed a small helper script named generate-schema in your local ./bin directory of your current environment (depending on whether you are using a virtual environment).

$ generate-schema < > file.schema.json

2) Python module

You can invoke the module directly using:

$ python3 -m bigquery_schema_generator.generate_schema < > file.schema.json

This is essentially what the generate-schema command does.

3) Python script

If you retrieved this code from its GitHub repository, then you can invoke the Python script directly:

$ ./ < > file.schema.json

Schema Output

The resulting schema file can be given to the bq load command using the --schema flag:

$ bq load --source_format NEWLINE_DELIMITED_JSON \
        --ignore_unknown_values \
        --schema file.schema.json \
        mydataset.mytable \

where mydataset.mytable is the target table in BigQuery.

For debugging purposes, here is the equivalent bq load command using schema autodetection:

$ bq load --source_format NEWLINE_DELIMITED_JSON \
    --ignore_unknown_values \
    mydataset.mytable \

A useful flag for bq load is --ignore_unknown_values, which causes bq load to ignore fields in the input data which are not defined in the schema. When detects an inconsistency in the definition of a particular field in the input data, it removes the field from the schema definition. Without the --ignore_unknown_values, the bq load fails when the inconsistent data record is read. Another useful flag during development and debugging is --replace which replaces any existing BigQuery table.

After the BigQuery table is loaded, the schema can be retrieved using:

$ bq show --schema mydataset.mytable | python -m json.tool

(The python -m json.tool command will pretty-print the JSON formatted schema file.) This schema file should be identical to file.schema.json.

Flag Options

The script supports a handful of command line flags:

  • --help Prints the usage with the list of supported flags.

  • --keep_nulls Print the schema for null values, empty arrays or empty records.

  • --debugging_interval lines Number of lines between heartbeat debugging messages. Default 1000.

  • --debugging_map Print the metadata schema map for debugging purposes

Help (--help)

Print the built-in help strings:

$ generate-schema --help
usage: [-h] [--keep_nulls]
                          [--debugging_interval DEBUGGING_INTERVAL]

Generate BigQuery schema.

optional arguments:
  -h, --help            show this help message and exit
  --keep_nulls          Print the schema for null values, empty arrays or
                        empty records.
  --debugging_interval DEBUGGING_INTERVAL
                        Number of lines between heartbeat debugging messages.
  --debugging_map       Print the metadata schema_map instead of the schema
                        for debugging

Keep Nulls (--keep_nulls)

Normally when the input data file contains a field which has a null, empty array or empty record as its value, the field is suppressed in the schema file. This flag enables this field to be included in the schema file.

In other words, using a data file containing just nulls and empty values:

$ generate_schema
{ "s": null, "a": [], "m": {} }
INFO:root:Processed 1 lines

With the keep_nulls flag, we get:

$ generate-schema --keep_nulls
{ "s": null, "a": [], "m": {} }
INFO:root:Processed 1 lines
    "mode": "REPEATED",
    "type": "STRING",
    "name": "a"
    "mode": "NULLABLE",
    "fields": [
        "mode": "NULLABLE",
        "type": "STRING",
        "name": "__unknown__"
    "type": "RECORD",
    "name": "d"
    "mode": "NULLABLE",
    "type": "STRING",
    "name": "s"

Debugging Interval (--debugging_interval)

By default, the script prints a short progress message every 1000 lines of input data. This interval can be changed using the --debugging_interval flag.

$ generate-schema --debugging_interval 50 < > file.schema.json

Debugging Map (--debugging_map)

Instead of printing out the BigQuery schema, the --debugging_map prints out the bookkeeping metadata map which is used internally to keep track of the various fields and their types that were inferred using the data file. This flag is intended to be used for debugging.

$ generate-schema --debugging_map < > file.schema.json

Schema Types

Supported Types

The bq show –schema command produces a JSON schema file that uses the older Legacy SQL date types. For compatibility, generate-schema script will also generate a schema file using the legacy data types.

The supported types are:






  • DATE

  • TIME


The generate-schema script supports both NULLABLE and REPEATED modes of all of the above types. The following types are not supported:


  • DATETIME (unable to distinguish from TIMESTAMP)

Type Inferrence Rules

The generate-schema script attempts to emulate the various type conversion and compatibility rules implemented by bq load:

  • INTEGER can upgrade to FLOAT

    • if a field in an early record is an INTEGER, but a subsequent record shows this field to have a FLOAT value, the type of the field will be upgraded to a FLOAT

    • the reverse does not happen, once a field is a FLOAT, it will remain a FLOAT

  • conflicting TIME, DATE, TIMESTAMP types upgrades to STRING

    • if a field is determined to have one type of “time” in one record, then subsequently a different “time” type, then the field will be assigned a STRING type


    • a field may be defined as RECORD (aka “Struct”) type with { ... }

    • if the field is subsequently read as an array with a [{ ... }], the field is upgraded to a REPEATED RECORD

  • a primitive type (FLOAT, INTEGER, STRING) cannot upgrade to a REPEATED primitive type

    • there’s no technical reason why this cannot be allowed, but bq load does not support it, so we follow the rule

  • a DATETIME field is always inferred to be a TIMESTAMP

    • the format of these two fields is identical (in the absence of timezone)

    • we follow the same logic as bq load and always infer these as TIMESTAMP

The BigQuery loader looks inside string values to determine if they are actually BOOLEAN, INTEGER or FLOAT types instead. In other words, "True" is considered a BOOLEAN type, "1" is considered an INTEGER type, and "2.1" is consiered a FLOAT type. Luigi Mori (jtschichold@) added additional logic to replicate the type conversion logic used by bq load for these strings.


Here is an example of a single JSON data record on the STDIN (the ^D below means typing Control-D, which indicates “end of file” under Linux and MacOS):

$ generate-schema
{ "s": "string", "b": true, "i": 1, "x": 3.1, "t": "2017-05-22T17:10:00-07:00" }
INFO:root:Processed 1 lines
    "mode": "NULLABLE",
    "name": "b",
    "type": "BOOLEAN"
    "mode": "NULLABLE",
    "name": "i",
    "type": "INTEGER"
    "mode": "NULLABLE",
    "name": "s",
    "type": "STRING"
    "mode": "NULLABLE",
    "name": "t",
    "type": "TIMESTAMP"
    "mode": "NULLABLE",
    "name": "x",
    "type": "FLOAT"

In most cases, the data file will be stored in a file:

$ cat >
{ "a": [1, 2] }
{ "i": 3 }

$ generate-schema < > file.schema.json
INFO:root:Processed 2 lines

$ cat file.schema.json
    "mode": "REPEATED",
    "name": "a",
    "type": "INTEGER"
    "mode": "NULLABLE",
    "name": "i",
    "type": "INTEGER"


I wrote the bigquery_schema_generator/ script to create an anonymized data file tests/testdata/

$ ./bigquery_schema_generator/ < \
$ gzip

This data file is 290MB (5.6MB compressed) with 103080 data records.

Generating the schema using

$ bigquery_schema_generator/ < \
    > anon1.schema.json

took 77s on a Dell Precision M4700 laptop with an Intel Core i7-3840QM CPU @ 2.80GHz, 32GB of RAM, Ubuntu Linux 17.10, Python 3.6.3.

System Requirements

This project was initially developed on Ubuntu 17.04 using Python 3.5.3. I have tested it on:

  • Ubuntu 18.04, Python 3.6.7

  • Ubuntu 17.10, Python 3.6.3

  • Ubuntu 17.04, Python 3.5.3

  • Ubuntu 16.04, Python 3.5.2

  • MacOS 10.13.2, Python 3.6.4


  • Created by Brian T. Park (

  • Additional type inferrence logic by Luigi Mori (jtschichold@).


Apache License 2.0

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